diff --git a/python-package/xgboost/training.py b/python-package/xgboost/training.py
index 03e24bdba..ca76bbe2b 100644
--- a/python-package/xgboost/training.py
+++ b/python-package/xgboost/training.py
@@ -292,8 +292,8 @@ def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True):
 
 
 def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
-       obj=None, feval=None, fpreproc=None, as_pandas=True,
-       show_progress=None, show_stdv=True, seed=0):
+       obj=None, feval=None, maximize=False, early_stopping_rounds=None,
+       fpreproc=None, as_pandas=True, show_progress=None, show_stdv=True, seed=0):
     # pylint: disable = invalid-name
     """Cross-validation with given paramaters.
 
@@ -313,6 +313,12 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
         Custom objective function.
     feval : function
         Custom evaluation function.
+    maximize : bool
+        Whether to maximize feval.
+    early_stopping_rounds: int
+        Activates early stopping. CV error needs to decrease at least
+        every <early_stopping_rounds> round(s) to continue.
+        Last entry in evaluation history is the one from best iteration.
     fpreproc : function
         Preprocessing function that takes (dtrain, dtest, param) and returns
         transformed versions of those.
@@ -332,6 +338,28 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
     -------
     evaluation history : list(string)
     """
+    if early_stopping_rounds is not None:
+        if len(metrics) > 1:
+            raise ValueError('Check your params.'\
+                                     'Early stopping works with single eval metric only.')
+
+        sys.stderr.write("Will train until cv error hasn't decreased in {} rounds.\n".format(\
+            early_stopping_rounds))
+
+        maximize_score = False
+        if len(metrics) == 1:
+            maximize_metrics = ('auc', 'map', 'ndcg')
+            if any(metrics[0].startswith(x) for x in maximize_metrics):
+                maximize_score = True
+        if feval is not None:
+            maximize_score = maximize
+
+        if maximize_score:
+            best_score = 0.0
+        else:
+            best_score = float('inf')
+
+    best_score_i = 0
     results = []
     cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc)
     for i in range(num_boost_round):
@@ -342,6 +370,17 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
                     as_pandas=as_pandas)
         results.append(res)
 
+        if early_stopping_rounds is not None:
+            score = res[0]
+            if (maximize_score and score > best_score) or \
+                    (not maximize_score and score < best_score):
+                best_score = score
+                best_score_i = i
+            elif i - best_score_i >= early_stopping_rounds:
+                sys.stderr.write("Stopping. Best iteration: {}\n".format(best_score_i))
+                results = results[:best_score_i+1]
+                break
+
     if as_pandas:
         try:
             import pandas as pd