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, helping to identify how far off the model is. The goal is to minimize this difference, allowing the model to improve its accuracy over time. 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 during training, models can learn patterns in the data and make better predictions on unseen data.