diff --git a/src/sagemaker/chainer/model.py b/src/sagemaker/chainer/model.py
index 3d5c645c73..b1d58eceb9 100644
--- a/src/sagemaker/chainer/model.py
+++ b/src/sagemaker/chainer/model.py
@@ -169,6 +169,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -216,6 +217,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             str: A string of SageMaker Model Package ARN.
@@ -254,6 +257,7 @@ def register(
             framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(
diff --git a/src/sagemaker/estimator.py b/src/sagemaker/estimator.py
index 38fd60d4e3..b757994fd5 100644
--- a/src/sagemaker/estimator.py
+++ b/src/sagemaker/estimator.py
@@ -1684,6 +1684,7 @@ def register(
         framework_version=None,
         nearest_model_name=None,
         data_input_configuration=None,
+        skip_model_validation=None,
         **kwargs,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
@@ -1729,6 +1730,8 @@ def register(
             nearest_model_name (str): Name of a pre-trained machine learning benchmarked by
                 Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str): Input object for the model (default: None).
+            skip_model_validation (str): Indicates if you want to skip model validation.
+                Values can be "All" or "None" (default: None).
             **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
                 ``create_model()`` to accept ``**kwargs`` to customize model creation during
                 deploy. For more, see the implementation docs.
@@ -1772,6 +1775,7 @@ def register(
             framework_version=framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     @property
diff --git a/src/sagemaker/huggingface/model.py b/src/sagemaker/huggingface/model.py
index 74eeeb2546..7f733bfd11 100644
--- a/src/sagemaker/huggingface/model.py
+++ b/src/sagemaker/huggingface/model.py
@@ -353,6 +353,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -401,6 +402,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -447,6 +450,7 @@ def register(
             ],
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(
diff --git a/src/sagemaker/model.py b/src/sagemaker/model.py
index d0f833795c..5dea4cdd29 100644
--- a/src/sagemaker/model.py
+++ b/src/sagemaker/model.py
@@ -400,6 +400,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -447,6 +448,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance or pipeline step arguments
@@ -497,6 +500,7 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
+            skip_model_validation=skip_model_validation,
         )
         model_package = self.sagemaker_session.create_model_package_from_containers(
             **model_pkg_args
diff --git a/src/sagemaker/mxnet/model.py b/src/sagemaker/mxnet/model.py
index 6983fa64b9..199adead2a 100644
--- a/src/sagemaker/mxnet/model.py
+++ b/src/sagemaker/mxnet/model.py
@@ -171,6 +171,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -218,6 +219,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -256,6 +259,7 @@ def register(
             framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(
diff --git a/src/sagemaker/pipeline.py b/src/sagemaker/pipeline.py
index 8c209d1280..d48f3b9cc5 100644
--- a/src/sagemaker/pipeline.py
+++ b/src/sagemaker/pipeline.py
@@ -356,6 +356,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -403,6 +404,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -448,6 +451,7 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
+            skip_model_validation=skip_model_validation,
         )
 
         self.sagemaker_session.create_model_package_from_containers(**model_pkg_args)
diff --git a/src/sagemaker/pytorch/model.py b/src/sagemaker/pytorch/model.py
index ce3181688f..09b9ee0c68 100644
--- a/src/sagemaker/pytorch/model.py
+++ b/src/sagemaker/pytorch/model.py
@@ -173,6 +173,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -220,6 +221,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -258,6 +261,7 @@ def register(
             framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(
diff --git a/src/sagemaker/session.py b/src/sagemaker/session.py
index 6c4496a2af..3cc677d2dc 100644
--- a/src/sagemaker/session.py
+++ b/src/sagemaker/session.py
@@ -3648,6 +3648,7 @@ def create_model_package_from_containers(
         domain=None,
         sample_payload_url=None,
         task=None,
+        skip_model_validation="None",
     ):
         """Get request dictionary for CreateModelPackage API.
 
@@ -3682,6 +3683,8 @@ def create_model_package_from_containers(
             task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
                 "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
                 "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+            skip_model_validation (str): Indicates if you want to skip model validation.
+                Values can be "All" or "None" (default: None).
         """
         if containers:
             # Containers are provided. Now we can merge missing entries from config.
@@ -3737,6 +3740,7 @@ def create_model_package_from_containers(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
+            skip_model_validation=skip_model_validation,
         )
 
         def submit(request):
@@ -5764,6 +5768,7 @@ def get_model_package_args(
     domain=None,
     sample_payload_url=None,
     task=None,
+    skip_model_validation=None,
 ):
     """Get arguments for create_model_package method.
 
@@ -5800,6 +5805,8 @@ def get_model_package_args(
         task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
             "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
             "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+        skip_model_validation (str): Indicates if you want to skip model validation.
+            Values can be "All" or "None" (default: None).
 
     Returns:
         dict: A dictionary of method argument names and values.
@@ -5848,6 +5855,8 @@ def get_model_package_args(
         model_package_args["sample_payload_url"] = sample_payload_url
     if task is not None:
         model_package_args["task"] = task
+    if skip_model_validation is not None:
+        model_package_args["skip_model_validation"] = skip_model_validation
     return model_package_args
 
 
@@ -5871,6 +5880,7 @@ def get_create_model_package_request(
     domain=None,
     sample_payload_url=None,
     task=None,
+    skip_model_validation="None",
 ):
     """Get request dictionary for CreateModelPackage API.
 
@@ -5905,6 +5915,8 @@ def get_create_model_package_request(
         task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
             "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
             "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+        skip_model_validation (str): Indicates if you want to skip model validation.
+            Values can be "All" or "None" (default: None).
     """
 
     if all([model_package_name, model_package_group_name]):
@@ -5974,6 +5986,7 @@ def get_create_model_package_request(
         request_dict["InferenceSpecification"] = inference_specification
     request_dict["CertifyForMarketplace"] = marketplace_cert
     request_dict["ModelApprovalStatus"] = approval_status
+    request_dict["SkipModelValidation"] = skip_model_validation
     return request_dict
 
 
diff --git a/src/sagemaker/sklearn/model.py b/src/sagemaker/sklearn/model.py
index 46425c0660..9b1ea73090 100644
--- a/src/sagemaker/sklearn/model.py
+++ b/src/sagemaker/sklearn/model.py
@@ -166,6 +166,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -213,6 +214,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -251,6 +254,7 @@ def register(
             framework_version=framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(
diff --git a/src/sagemaker/tensorflow/model.py b/src/sagemaker/tensorflow/model.py
index 705b7667dd..97b0e51b01 100644
--- a/src/sagemaker/tensorflow/model.py
+++ b/src/sagemaker/tensorflow/model.py
@@ -228,6 +228,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -275,6 +276,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -313,6 +316,7 @@ def register(
             framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def deploy(
diff --git a/src/sagemaker/workflow/_utils.py b/src/sagemaker/workflow/_utils.py
index b7372237e3..8b30823d06 100644
--- a/src/sagemaker/workflow/_utils.py
+++ b/src/sagemaker/workflow/_utils.py
@@ -322,6 +322,7 @@ def __init__(
         domain=None,
         sample_payload_url=None,
         task=None,
+        skip_model_validation=None,
         **kwargs,
     ):
         """Constructor of a register model step.
@@ -371,6 +372,8 @@ def __init__(
             task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
                 "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
                 "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+            skip_model_validation (str): Indicates if you want to skip model validation.
+                Values can be "All" or "None" (default: None).
             **kwargs: additional arguments to `create_model`.
         """
         super(_RegisterModelStep, self).__init__(
@@ -407,6 +410,7 @@ def __init__(
         self.tags = tags
         self.kwargs = kwargs
         self.container_def_list = container_def_list
+        self.skip_model_validation = skip_model_validation
 
         self._properties = Properties(step_name=name, shape_name="DescribeModelPackageOutput")
 
@@ -481,6 +485,7 @@ def arguments(self) -> RequestType:
                 domain=self.domain,
                 sample_payload_url=self.sample_payload_url,
                 task=self.task,
+                skip_model_validation=self.skip_model_validation,
             )
 
             request_dict = get_create_model_package_request(**model_package_args)
diff --git a/src/sagemaker/workflow/step_collections.py b/src/sagemaker/workflow/step_collections.py
index 8b6da2ae93..405f910639 100644
--- a/src/sagemaker/workflow/step_collections.py
+++ b/src/sagemaker/workflow/step_collections.py
@@ -90,6 +90,7 @@ def __init__(
         framework_version=None,
         nearest_model_name=None,
         data_input_configuration=None,
+        skip_model_validation=None,
         **kwargs,
     ):
         """Construct steps `_RepackModelStep` and `_RegisterModelStep` based on the estimator.
@@ -145,6 +146,8 @@ def __init__(
             nearest_model_name (str): Name of a pre-trained machine learning benchmarked by
                 Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str): Input object for the model (default: None).
+            skip_model_validation (str): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
             **kwargs: additional arguments to `create_model`.
         """
@@ -281,6 +284,7 @@ def __init__(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
+            skip_model_validation=skip_model_validation,
             **kwargs,
         )
         if not repack_model:
diff --git a/src/sagemaker/xgboost/model.py b/src/sagemaker/xgboost/model.py
index e2be025b62..a40151c925 100644
--- a/src/sagemaker/xgboost/model.py
+++ b/src/sagemaker/xgboost/model.py
@@ -154,6 +154,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -201,6 +202,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             str: A string of SageMaker Model Package ARN.
@@ -239,6 +242,7 @@ def register(
             framework_version=framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(
diff --git a/tests/integ/sagemaker/workflow/test_model_create_and_registration.py b/tests/integ/sagemaker/workflow/test_model_create_and_registration.py
index 98ee4bfde6..75e086bcfe 100644
--- a/tests/integ/sagemaker/workflow/test_model_create_and_registration.py
+++ b/tests/integ/sagemaker/workflow/test_model_create_and_registration.py
@@ -600,6 +600,7 @@ def test_model_registration_with_drift_check_baselines(
     framework_version = "2.9"
     nearest_model_name = "resnet50"
     data_input_configuration = '{"input_1":[1,224,224,3]}'
+    skip_model_validation = "All"
 
     # If image_uri is not provided, the instance_type should not be a pipeline variable
     # since instance_type is used to retrieve image_uri in compile time (PySDK)
@@ -633,6 +634,7 @@ def test_model_registration_with_drift_check_baselines(
         framework_version=framework_version,
         nearest_model_name=nearest_model_name,
         data_input_configuration=data_input_configuration,
+        skip_model_validation=skip_model_validation,
     )
 
     pipeline = Pipeline(
@@ -703,6 +705,7 @@ def test_model_registration_with_drift_check_baselines(
             assert response["Domain"] == domain
             assert response["Task"] == task
             assert response["SamplePayloadUrl"] == sample_payload_url
+            assert response["SkipModelValidation"] == skip_model_validation
             break
     finally:
         try:
diff --git a/tests/unit/sagemaker/workflow/test_pipeline_session.py b/tests/unit/sagemaker/workflow/test_pipeline_session.py
index b6c17033ed..d05bb5c20f 100644
--- a/tests/unit/sagemaker/workflow/test_pipeline_session.py
+++ b/tests/unit/sagemaker/workflow/test_pipeline_session.py
@@ -218,6 +218,7 @@ def test_pipeline_session_context_for_model_step_without_instance_types(
         },
         "CertifyForMarketplace": False,
         "ModelApprovalStatus": "PendingManualApproval",
+        "SkipModelValidation": "None",
         "SamplePayloadUrl": "s3://test-bucket/model",
         "Task": "IMAGE_CLASSIFICATION",
     }
@@ -280,6 +281,7 @@ def test_pipeline_session_context_for_model_step_with_one_instance_types(
         },
         "CertifyForMarketplace": False,
         "ModelApprovalStatus": "PendingManualApproval",
+        "SkipModelValidation": "None",
         "SamplePayloadUrl": "s3://test-bucket/model",
         "Task": "IMAGE_CLASSIFICATION",
     }
diff --git a/tests/unit/sagemaker/workflow/test_step_collections.py b/tests/unit/sagemaker/workflow/test_step_collections.py
index ddbd062f17..847a2d3656 100644
--- a/tests/unit/sagemaker/workflow/test_step_collections.py
+++ b/tests/unit/sagemaker/workflow/test_step_collections.py
@@ -498,6 +498,7 @@ def test_register_model(estimator, model_metrics, drift_check_baselines):
                         "SupportedTransformInstanceTypes": ["transform_instance"],
                     },
                     "ModelApprovalStatus": "Approved",
+                    "SkipModelValidation": "None",
                     "ModelMetrics": {
                         "Bias": {},
                         "Explainability": {},
@@ -568,6 +569,7 @@ def test_register_model_tf(estimator_tf, model_metrics, drift_check_baselines):
                         "SupportedTransformInstanceTypes": ["transform_instance"],
                     },
                     "ModelApprovalStatus": "Approved",
+                    "SkipModelValidation": "None",
                     "ModelMetrics": {
                         "Bias": {},
                         "Explainability": {},
@@ -657,6 +659,7 @@ def test_register_model_sip(estimator, model_metrics, drift_check_baselines):
                         "SupportedTransformInstanceTypes": ["transform_instance"],
                     },
                     "ModelApprovalStatus": "Approved",
+                    "SkipModelValidation": "None",
                     "ModelMetrics": {
                         "Bias": {},
                         "Explainability": {},
@@ -794,6 +797,7 @@ def test_register_model_with_model_repack_with_estimator(
                         "SupportedTransformInstanceTypes": ["transform_instance"],
                     },
                     "ModelApprovalStatus": "Approved",
+                    "SkipModelValidation": "None",
                     "ModelMetrics": {
                         "Bias": {},
                         "Explainability": {},
@@ -919,6 +923,7 @@ def test_register_model_with_model_repack_with_model(model, model_metrics, drift
                         "SupportedTransformInstanceTypes": ["transform_instance"],
                     },
                     "ModelApprovalStatus": "Approved",
+                    "SkipModelValidation": "None",
                     "ModelMetrics": {
                         "Bias": {},
                         "Explainability": {},
@@ -1052,6 +1057,7 @@ def test_register_model_with_model_repack_with_pipeline_model(
                         "SupportedTransformInstanceTypes": ["transform_instance"],
                     },
                     "ModelApprovalStatus": "Approved",
+                    "SkipModelValidation": "None",
                     "ModelMetrics": {
                         "Bias": {},
                         "Explainability": {},
diff --git a/tests/unit/test_session.py b/tests/unit/test_session.py
index b07f90a55b..cf35c8e0fe 100644
--- a/tests/unit/test_session.py
+++ b/tests/unit/test_session.py
@@ -4621,6 +4621,7 @@ def test_create_model_package_from_containers_without_model_package_group_name(
 def test_create_model_package_with_sagemaker_config_injection(sagemaker_session):
     sagemaker_session.sagemaker_config = SAGEMAKER_CONFIG_MODEL_PACKAGE
 
+    skip_model_validation = "All"
     model_package_name = "sagemaker-model-package"
     containers = [{"Image": "dummy-container"}]
     content_types = ["application/json"]
@@ -4680,6 +4681,7 @@ def test_create_model_package_with_sagemaker_config_injection(sagemaker_session)
         sample_payload_url=sample_payload_url,
         task=task,
         validation_specification=validation_specification,
+        skip_model_validation=skip_model_validation,
     )
     expected_kms_key_id = SAGEMAKER_CONFIG_MODEL_PACKAGE["SageMaker"]["ModelPackage"][
         "ValidationSpecification"
@@ -4717,6 +4719,7 @@ def test_create_model_package_with_sagemaker_config_injection(sagemaker_session)
             "SamplePayloadUrl": sample_payload_url,
             "Task": task,
             "ValidationSpecification": validation_specification,
+            "SkipModelValidation": skip_model_validation,
         }
     )
     expected_args["ValidationSpecification"]["ValidationRole"] = expected_role_arn
@@ -4764,6 +4767,7 @@ def test_create_model_package_from_containers_all_args(sagemaker_session):
         "GeneratedBy": "sagemaker-python-sdk",
         "ProjectId": "unit-test",
     }
+    skip_model_validation = "All"
     marketplace_cert = (True,)
     approval_status = ("Approved",)
     description = "description"
@@ -4788,6 +4792,7 @@ def test_create_model_package_from_containers_all_args(sagemaker_session):
         domain=domain,
         sample_payload_url=sample_payload_url,
         task=task,
+        skip_model_validation=skip_model_validation,
     )
     expected_args = {
         "ModelPackageName": model_package_name,
@@ -4808,6 +4813,7 @@ def test_create_model_package_from_containers_all_args(sagemaker_session):
         "Domain": domain,
         "SamplePayloadUrl": sample_payload_url,
         "Task": task,
+        "SkipModelValidation": skip_model_validation,
     }
     sagemaker_session.sagemaker_client.create_model_package.assert_called_with(**expected_args)
 
@@ -4838,6 +4844,7 @@ def test_create_model_package_from_containers_without_instance_types(sagemaker_s
         "GeneratedBy": "sagemaker-python-sdk",
         "ProjectId": "unit-test",
     }
+    skip_model_validation = "All"
     marketplace_cert = (True,)
     approval_status = ("Approved",)
     description = "description"
@@ -4854,6 +4861,7 @@ def test_create_model_package_from_containers_without_instance_types(sagemaker_s
         description=description,
         drift_check_baselines=drift_check_baselines,
         customer_metadata_properties=customer_metadata_properties,
+        skip_model_validation=skip_model_validation,
     )
     expected_args = {
         "ModelPackageGroupName": model_package_group_name,
@@ -4869,6 +4877,7 @@ def test_create_model_package_from_containers_without_instance_types(sagemaker_s
         "ModelApprovalStatus": approval_status,
         "DriftCheckBaselines": drift_check_baselines,
         "CustomerMetadataProperties": customer_metadata_properties,
+        "SkipModelValidation": skip_model_validation,
     }
     sagemaker_session.sagemaker_client.create_model_package.assert_called_with(**expected_args)
 
@@ -4902,6 +4911,7 @@ def test_create_model_package_from_containers_with_one_instance_types(
         "GeneratedBy": "sagemaker-python-sdk",
         "ProjectId": "unit-test",
     }
+    skip_model_validation = "All"
     marketplace_cert = (True,)
     approval_status = ("Approved",)
     description = "description"
@@ -4919,6 +4929,7 @@ def test_create_model_package_from_containers_with_one_instance_types(
         description=description,
         drift_check_baselines=drift_check_baselines,
         customer_metadata_properties=customer_metadata_properties,
+        skip_model_validation=skip_model_validation,
     )
     expected_args = {
         "ModelPackageGroupName": model_package_group_name,
@@ -4935,6 +4946,7 @@ def test_create_model_package_from_containers_with_one_instance_types(
         "ModelApprovalStatus": approval_status,
         "DriftCheckBaselines": drift_check_baselines,
         "CustomerMetadataProperties": customer_metadata_properties,
+        "SkipModelValidation": skip_model_validation,
     }
     sagemaker_session.sagemaker_client.create_model_package.assert_called_with(**expected_args)