Ensemble learning combines multiple machine learning models to improve prediction accuracy. The idea is that combining the outputs of several models, such as decision trees, neural networks, or support vector machines, can produce better results than a single model. Techniques like bagging, boosting, and stacking are popular ensemble methods used to enhance model robustness and performance.