Stacking (Stacked Generalization) is an ensemble method that trains a “meta-learner” to combine predictions from multiple base models. The meta-learner takes the base learners’ predictions as inputs and learns the best way to blend them. It often outperforms simpler ensembles like bagging or boosting.