Model performance refers to the evaluation of how well a statistical model is able to predict or estimate outcomes based on the data it was trained on. This evaluation is typically done by comparing the model's predictions to actual outcomes and calculating various metrics such as accuracy, precision, recall, and F1 score. The goal of measuring model performance is to assess the effectiveness of the model in making accurate predictions and to identify areas where the model may need improvement. Model performance is crucial in machine learning and predictive modeling, as it helps to determine the reliability and effectiveness of the model in real-world applications.