A loss function is a mathematical tool used in machine learning to measure how well a model's predictions align with the actual outcomes. It quantifies the difference between the predicted values and the true values, guiding the model to improve its accuracy. The goal is to minimize this difference during the training process. Different types of loss functions exist, such as mean squared error for regression tasks and cross-entropy loss for classification tasks. By optimizing the loss function, algorithms like gradient descent can adjust model parameters to enhance performance and achieve better predictions.