A Loss Function is a function that measures the difference between the predicted output and the true output in a machine learning model. It quantifies the model's performance and provides feedback on how well it is approximating the desired output. The choice of a loss function depends on the learning task, such as mean squared error (MSE) for regression problems or cross-entropy loss for classification problems. The model aims to minimize the loss function during the training process.