Ensembles refer to a popular machine learning technique where multiple models are combined to improve prediction accuracy. These models can vary in terms of algorithms, parameters, or training data to produce diverse predictions. Ensembles are often used in various fields such as finance, healthcare, and marketing to tackle complex problems and generate more robust and accurate predictions. Common ensemble methods include bagging, boosting, and stacking. By leveraging the power of multiple models, ensembles can effectively reduce overfitting and improve overall prediction performance.