Mean Squared Error (MSE) is a statistical measure used to evaluate the accuracy of a model by calculating the average of the squares of the errors. An error is the difference between the predicted value and the actual value. By squaring these errors, MSE emphasizes larger discrepancies, making it useful for identifying models that perform poorly. MSE is commonly used in various fields, including machine learning and statistics, to assess the performance of regression models. A lower MSE indicates a better fit of the model to the data, while a higher MSE suggests that the model's predictions are less accurate.