"Model fit" refers to how well a statistical model represents the data it is intended to explain. It assesses the accuracy of predictions made by the model compared to the actual observed values. A good model fit indicates that the model captures the underlying patterns in the data effectively. Various metrics, such as R-squared and mean squared error, are used to evaluate model fit. A higher R-squared value suggests that a larger proportion of the variance in the data is explained by the model, while lower mean squared error values indicate better predictive accuracy.