Ensemble Learning is a technique where multiple models (often of different types) are trained to solve the same problem and combined to get better results. The idea is that a group of weak learners can come together to form a strong learner. Common ensemble methods include bagging (e.g., Random Forest), boosting (e.g., XGBoost, AdaBoost), and stacking. Ensemble learning improves the accuracy, robustness, and generalization of the model by reducing the variance and bias. It is widely used in machine learning competitions and practical applications to achieve superior predictive performance.