A loss function quantifies the difference between predicted and actual outcomes in a model. It guides the learning process by indicating areas to improve upon, often by minimizing the loss through optimization techniques like gradient descent. For example, in a classifier, the cross-entropy loss function helps adjust weights to reduce misclassification. Similar terms include cost function and objective function, which are sometimes used interchangeably.