Actually there is no need for a long loop iterations that are provided in the most upvoted answer. Also the choice of classifier is irrelevant (it can be any classifier).
Scikit provides cross_val_score, which does all the looping under the hood.
from sklearn.cross_validation import KFold, cross_val_score
k_fold = KFold(len(y), n_folds=10, shuffle=True, random_state=0)
clf = <any classifier>
print cross_val_score(clf, X, y, cv=k_fold, n_jobs=1)