A model’s capacity is its ability to fit a wide variety of functions. The model $\hat{f}$ that we estimate by choosing a functional form and training, is just an approximation and almost never match the true unknown $f$. To have a closer estimate, we can choose more flexible functional forms with more parameters to estimate. Machine learning algorithms will generally perform best when their capacity is appropriate for the true complexity of the task they need to perform and the amount of training data they are provided with. Models with insufficient capacity are unable to solve complex tasks. Models with high capacity can solve complex tasks, but when their capacity is higher than needed to solve the present task, they may overfit.