change k-fold note

Author: PatrickFarley

articles/cognitive-services/Custom-Vision-Service/export-programmatically.md

@@ -53,6 +53,9 @@ export = trainer.export_iteration(project_id, iteration_id, platform, flavor, ra
 
 For more information, see the **[export_iteration](/python/api/azure-cognitiveservices-vision-customvision/azure.cognitiveservices.vision.customvision.training.operations.customvisiontrainingclientoperationsmixin#export-iteration-project-id--iteration-id--platform--flavor-none--custom-headers-none--raw-false----operation-config-)** method.
 
+> [!IMPORTANT]
+> If you've already exported a particular iteration, you cannot call the **export_iteration** method again. Instead, skip ahead to the **get_exports** method call to get a link to your existing exported model.
+
 ## Download the exported model
 
 Next, you'll call the **get_exports** method to check the status of the export operation. The operation runs asynchronously, so you should poll this method until the operation completes. When it completes, you can retrieve the URI where you can download the model iteration to your device.

articles/cognitive-services/Custom-Vision-Service/get-started-build-detector.md

@@ -92,7 +92,7 @@ The training process should only take a few minutes. During this time, informati
 
 ## Evaluate the detector
 
-After training has completed, the model's performance is calculated and displayed. The Custom Vision service uses the images that you submitted for training to calculate precision, recall, and mean average precision, using a process called [k-fold cross validation](https://wikipedia.org/wiki/Cross-validation_(statistics)). Precision and recall are two different measurements of the effectiveness of a detector:
+After training has completed, the model's performance is calculated and displayed. The Custom Vision service uses the images that you submitted for training to calculate precision, recall, and mean average precision. Precision and recall are two different measurements of the effectiveness of a detector:
 
 - **Precision** indicates the fraction of identified classifications that were correct. For example, if the model identified 100 images as dogs, and 99 of them were actually of dogs, then the precision would be 99%.
 - **Recall** indicates the fraction of actual classifications that were correctly identified. For example, if there were actually 100 images of apples, and the model identified 80 as apples, the recall would be 80%.

articles/cognitive-services/Custom-Vision-Service/getting-started-build-a-classifier.md

@@ -95,7 +95,7 @@ The training process should only take a few minutes. During this time, informati
 
 ## Evaluate the classifier
 
-After training has completed, the model's performance is estimated and displayed. The Custom Vision Service uses the images that you submitted for training to calculate precision and recall, using a process called [k-fold cross validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)). Precision and recall are two different measurements of the effectiveness of a classifier:
+After training has completed, the model's performance is estimated and displayed. The Custom Vision Service uses the images that you submitted for training to calculate precision and recall. Precision and recall are two different measurements of the effectiveness of a classifier:
 
 - **Precision** indicates the fraction of identified classifications that were correct. For example, if the model identified 100 images as dogs, and 99 of them were actually of dogs, then the precision would be 99%.
 - **Recall** indicates the fraction of actual classifications that were correctly identified. For example, if there were actually 100 images of apples, and the model identified 80 as apples, the recall would be 80%.