Model Aggregation is a technique used in machine learning where multiple models are combined to improve overall performance. Instead of relying on a single model, this approach leverages the strengths of various models, which can lead to better accuracy and robustness in predictions. This process often involves averaging the predictions of different models or selecting the best-performing model based on specific criteria. Ensemble methods, such as bagging and boosting, are common examples of model aggregation, helping to reduce errors and enhance the reliability of the results in various applications.