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. These methods work on the principle that a group of diverse models can provide better predictions than any single model alone. For instance, Random Forest is an ensemble method that builds multiple decision trees and merges their results, while AdaBoost focuses on adjusting the weights of misclassified instances to improve future predictions.