diff --git a/mlxtend/feature_selection/sequential_feature_selector.py b/mlxtend/feature_selection/sequential_feature_selector.py
index e907a4ab1..6879c127e 100644
--- a/mlxtend/feature_selection/sequential_feature_selector.py
+++ b/mlxtend/feature_selection/sequential_feature_selector.py
@@ -18,15 +18,19 @@
 from sklearn.base import BaseEstimator
 from sklearn.base import MetaEstimatorMixin
 from ..externals.name_estimators import _name_estimators
-from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import cross_val_score, RepeatedKFold, RepeatedStratifiedKFold
 from sklearn.externals.joblib import Parallel, delayed
 
-
 def _calc_score(selector, X, y, indices):
     if selector.cv:
+        if selector.n_cv_repeats > 0:
+            if selector._estimator_type == 'classifier':
+                cv_folds = RepeatedStratifiedKFold(n_splits=selector.cv, n_repeats=selector.n_cv_repeats)
+            else:
+                cv_folds = RepeatedKFold(n_splits=selector.cv, n_repeats=selector.n_cv_repeats)  
         scores = cross_val_score(selector.est_,
                                  X[:, indices], y,
-                                 cv=selector.cv,
+                                 cv=selector.cv if selector.n_cv_repeats == 0 else cv_folds,
                                  scoring=selector.scorer,
                                  n_jobs=1,
                                  pre_dispatch=selector.pre_dispatch)
@@ -103,6 +107,11 @@ class SequentialFeatureSelector(BaseEstimator, MetaEstimatorMixin):
         if False. Set to False if the estimator doesn't
         implement scikit-learn's set_params and get_params methods.
         In addition, it is required to set cv=0, and n_jobs=1.
+    n_cv_repeats : int (default = 0)
+        The number of times cross-validation will be repeated. If 0 then it's 
+        not repeated. Negative numbers raise an exception. Uses Scikit-learn 
+        RepeatedStratifiedKFold for a classifier or RepeatedKFold otherwise. 
+
 
     Attributes
     ----------
@@ -125,7 +134,8 @@ def __init__(self, estimator, k_features=1,
                  verbose=0, scoring=None,
                  cv=5, n_jobs=1,
                  pre_dispatch='2*n_jobs',
-                 clone_estimator=True):
+                 clone_estimator=True,
+                 n_cv_repeats = 0):
 
         self.estimator = estimator
         self.k_features = k_features
@@ -149,6 +159,13 @@ def __init__(self, estimator, k_features=1,
             self.est_ = self.estimator
         self.scoring = scoring
 
+        self.n_cv_repeats = n_cv_repeats
+        if self.n_cv_repeats < 0:
+            raise AttributeError('Number of cross-validation repeats should be >= 0.')
+        if not self.cv and self.n_cv_repeats > 0:
+            raise AttributeError('Cannot repeat cross-validation when it\'s set to 0.')
+
+
         if scoring is None:
             if self.est_._estimator_type == 'classifier':
                 scoring = 'accuracy'