Boosting is an ensemble technique where models are added sequentially, each one correcting the mistakes of the previous ensemble. It typically uses weak learners (e.g., small decision trees) but can produce strong predictive performance. Popular implementations include AdaBoost, Gradient Boosting, and XGBoost.