A random forest combines many decision trees trained on samples drawn with replacement from the data and on random subsets of features. The predictions are aggregated (vote or mean), which reduces the variance and the overfitting of a single tree. It is robust and requires little tuning.
Example
RandomForestClassifier(n_estimators=300, random_state=42)