Ensemble methods are techniques in machine learning that combine multiple models to improve overall performance. By aggregating the predictions of several models, these methods can reduce errors and increase accuracy. Common ensemble techniques include bagging, boosting, and stacking, each utilizing different strategies to enhance model robustness. One popular ensemble method is Random Forest, which builds multiple decision trees and merges their results for more reliable predictions. Another is AdaBoost, which focuses on correcting the errors of weaker models by giving them more weight in the final prediction. These methods are widely used in various applications due to their effectiveness.