Model fitting is the process of adjusting a statistical model to best represent a set of data. This involves selecting a model type, such as linear regression or decision trees, and then using algorithms to find the parameters that minimize the difference between the model's predictions and the actual data points. Once the model is fitted, it can be used to make predictions or understand relationships within the data. The quality of the fit is often evaluated using metrics like R-squared or mean squared error, which help determine how well the model captures the underlying patterns in the data.