diff --git a/python-package/xgboost/training.py b/python-package/xgboost/training.py
index fd7565e52..bfb39e837 100644
--- a/python-package/xgboost/training.py
+++ b/python-package/xgboost/training.py
@@ -361,7 +361,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
         Number of boosting iterations.
     nfold : int
         Number of folds in CV.
-    metrics : list of strings
+    metrics : string or list of strings
         Evaluation metrics to be watched in CV.
     obj : function
         Custom objective function.
@@ -394,9 +394,28 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
     -------
     evaluation history : list(string)
     """
+    if isinstance(metrics, str):
+        metrics = [metrics]
+
+    if isinstance(params, list):
+        _metrics = [x[1] for x in params if x[0] == 'eval_metric']
+        params = dict(params)
+        if 'eval_metric' in params:
+            params['eval_metric'] = _metrics
+    else:
+        params= dict((k, v) for k, v in params.items())
+
+    if len(metrics) == 0 and 'eval_metric' in params:
+        if isinstance(params['eval_metric'], list):
+            metrics = params['eval_metric']
+        else:
+            metrics = [params['eval_metric']]
+
+    params.pop("eval_metric", None)
+
     if early_stopping_rounds is not None:
         if len(metrics) > 1:
-            raise ValueError('Check your params.'\
+            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(\
@@ -434,7 +453,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
                 best_score_i = i
             elif i - best_score_i >= early_stopping_rounds:
                 results = results[:best_score_i+1]
-                sys.stderr.write("Stopping. Best iteration: {} (mean: {}, std: {})\n".
+                sys.stderr.write("Stopping. Best iteration:\n[{}] cv-mean:{}\tcv-std:{}\n".
                                  format(best_score_i, results[-1][0], results[-1][1]))
                 break
     if as_pandas: