Scikit learn GridSearchCV AUC performance

Question

I'm using GridSearchCV to identify the best set of parameters for a random forest classifier.

PARAMS = {
    'max_depth': [8,None],
    'n_estimators': [500,1000]
}
rf = RandomForestClassifier()
clf = grid_search.GridSearchCV(estimator=rf, param_grid=PARAMS, scoring='roc_auc', cv=5, n_jobs=4)
clf.fit(data, labels)

where data and labels are respectively the full dataset and the corresponding labels.

Now, I compared the performance returned by the GridSearchCV (from clf.grid_scores_) with a "manual" AUC estimation:

aucs = []
for fold in range (0,n_folds):
    probabilities = []
    train_data,train_label = read_data(train_file_fold)
    test_data,test_labels = read_data(test_file_fold)
    clf = RandomForestClassifier(n_estimators = 1000,max_depth=8)
    clf = clf.fit(train_data,train_labels)
    predicted_probs = clf.predict_proba(test_data)
    for value in predicted_probs:
       for k, pr in enumerate(value):
            if k == 1:
                probabilities.append(pr)
    fpr, tpr, thresholds = metrics.roc_curve(test_labels, probabilities, pos_label=1)   
    fold_auc = metrics.auc(fpr, tpr)
    aucs.append(fold_auc)

performance = np.mean(aucs)

where I manually pre-split the data into training and test set (same 5 CV approach).

The AUC values returned by GridSearchCV are always higher than the one manually calculated (e.g. 0.62 vs. 0.70) when using the same parameter for RandomForest. I know that different training and test split might give you different performance but this occurred constantly when testing 100 repetitions of the GridSearchCV. Interesting, if I use the accuarcy instead of roc_auc as scoring metric, the difference in performance is minimal and can be associated to the fact that I use different training and test set. Is this happening because the AUC value of GridSearchCV is estimated in a different way than by using metrics.roc_curve?