diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 6b0a9ffde9f42..9594cb344ec18 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -66,6 +66,9 @@ def evaluate(self, dataset, params=None):
         else:
             raise ValueError("Params must be a param map but got %s." % type(params))
 
+    def isLargerBetter(self):
+        return True
+
 
 @inherit_doc
 class JavaEvaluator(Evaluator, JavaWrapper):
@@ -85,6 +88,9 @@ def _evaluate(self, dataset):
         self._transfer_params_to_java()
         return self._java_obj.evaluate(dataset._jdf)
 
+    def isLargerBetter(self):
+        return self._java_obj.isLargerBetter()
+
 
 @inherit_doc
 class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol):
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index dcfee6a3170ab..7f6bbebfe0f63 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -223,7 +223,11 @@ def _fit(self, dataset):
                 # TODO: duplicate evaluator to take extra params from input
                 metric = eva.evaluate(model.transform(validation, epm[j]))
                 metrics[j] += metric
-        bestIndex = np.argmax(metrics)
+        
+        if eva.isLargerBetter():
+            bestIndex = np.argmax(metrics)
+        else:
+            bestIndex = np.argmin(metrics)
         bestModel = est.fit(dataset, epm[bestIndex])
         return CrossValidatorModel(bestModel)