What are ensemble methods, and why are they useful?

Ensemble methods combine multiple models to improve overall performance. By aggregating the predictions of several base models, ensemble methods reduce variance (bagging), bias (boosting), or both (stacking). Popular ensemble methods include Random Forests, AdaBoost, and Gradient Boosting Machines.