Ensemble learning combines multiple models to produce predictions that are more accurate and robust than any single model alone. Random forests aggregate hundreds of decision trees; gradient boosting machines sequentially correct errors from previous models. The wisdom-of-crowds principle: each model has different weaknesses, so averaging or voting cancels out individual errors. Ensembles dominate Kaggle competitions and are widely used in credit scoring, fraud detection, and demand forecasting.