Ensemble techniques are methods in machine learning that combine multiple models to improve overall performance. By aggregating the predictions from different models, these techniques can reduce errors and increase accuracy. Common ensemble methods include bagging, boosting, and stacking, each utilizing different strategies to enhance model predictions. These techniques work on the principle that a group of diverse models can provide better results than a single model. For instance, Random Forest is a popular ensemble method that uses decision trees to create a robust model by averaging their predictions, thus minimizing the risk of overfitting.