Mean Squared Error (MSE) is a common metric used to measure the accuracy of a model's predictions. It calculates the average of the squares of the differences between predicted values and actual values. By squaring the errors, MSE emphasizes larger errors, making it useful for identifying models that perform poorly. MSE is widely used in various fields, including machine learning, statistics, and data analysis. 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.