diff --git a/h2o-py/h2o/model/metrics/binomial.py b/h2o-py/h2o/model/metrics/binomial.py
index 23130234a84b..260d34e3bc6e 100644
--- a/h2o-py/h2o/model/metrics/binomial.py
+++ b/h2o-py/h2o/model/metrics/binomial.py
@@ -976,3 +976,26 @@ def thresholds_and_metric_scores(self):
         if 'thresholds_and_metric_scores' in self._metric_json:
             return self._metric_json['thresholds_and_metric_scores']
         return None
+    
+    def kolmogorov_smirnov(self, thresholds= None):
+        """
+        :param thresholds: thresholds parameter must be a list (e.g. ``[0.01, 0.5, 0.99]``).
+            If None, then the threshold maximizing the KS statistic will be used.
+        :returns: The Kolmogorov-Smirnov statistic for this set of metrics and thresholds.
+
+        :examples:
+
+        >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
+        >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
+        >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
+        >>> predictors = ["displacement","power","weight","acceleration","year"]
+        >>> response = "economy_20mpg"
+        >>> train, valid = cars.split_frame(ratios = [.8], seed = 1234)
+        >>> cars_gbm = H2OGradientBoostingEstimator(seed = 1234)
+        >>> cars_gbm.train(x = predictors,
+        ...                y = response,
+        ...                training_frame = train,
+        ...                validation_frame = valid)
+        >>> cars_gbm.kolmogorov_smirnov()
+        """
+        return self.metric("ks", thresholds=thresholds)
diff --git a/h2o-py/tests/pyunit_math/pyunit_ks_metric.py b/h2o-py/tests/pyunit_math/pyunit_ks_metric.py
index c8f90f5de3b8..98f35633fd93 100644
--- a/h2o-py/tests/pyunit_math/pyunit_ks_metric.py
+++ b/h2o-py/tests/pyunit_math/pyunit_ks_metric.py
@@ -10,7 +10,8 @@ def kolmogorov_smirnov():
     model = H2OGradientBoostingEstimator(ntrees=1, gainslift_bins=20)
     model.train(x=["Origin", "Distance"], y="IsDepDelayed", training_frame=airlines)
     verify_ks(model, airlines)
-
+    
+    # Test without Thresholds
     model = H2OGradientBoostingEstimator(ntrees=1, gainslift_bins=5)
     model.train(x=["Origin", "Distance"], y="IsDepDelayed", training_frame=airlines)
     ks = model.kolmogorov_smirnov()
@@ -19,6 +20,21 @@ def kolmogorov_smirnov():
     print(ks_verification)
     assert round(ks, 5) != round(ks_verification, 5)
 
+    # Test with specific thresholds
+    model = H2OGradientBoostingEstimator(ntrees=1, gainslift_bins=5)
+    model.train(x=["Origin", "Distance"], y="IsDepDelayed", training_frame=airlines)
+    ks = model.kolmogorov_smirnov(thresholds=[0.01, 0.5, 0.99])
+    print("KS with thresholds [0.01, 0.5, 0.99]:", ks)
+    ks_verification = ks_metric(model, airlines)
+    print("KS verification:", ks_verification)
+    assert round(ks, 5) != round(ks_verification, 5)
+
+    # Test with invalid Thresholds
+    try:
+        ks= model.kolmogorov_smirnov(thresholds= "invalid")
+    except ValueError as e:
+        print("Caught excepted exception for invalid thresholds:",e)
+        
     model = H2OXGBoostEstimator(gainslift_bins=10)
     model.train(x=["Origin", "Distance"], y="IsDepDelayed", training_frame=airlines)
     print(model.gains_lift())