In supervised learning, the goal is to learn from labeled data. The model is trained using a set of inputs where each input is labeled with the correct output. The performance of the model is measured using a loss function, which quantifies the difference between the model's prediction and the actual label. This process involves adjusting the model's parameters to minimize this loss, effectively teaching the model to replicate the labeling in the training data.