Supervised Learning is a type of machine learning where the model is trained on labeled data. In this approach, the training dataset includes input-output pairs, where the input data is accompanied by the correct output. The model learns to map inputs to outputs by minimizing the difference between its predictions and the actual values during training. Common supervised learning tasks include classification and regression. Supervised learning algorithms, such as linear regression, decision trees, and neural networks, are used in various applications like spam detection, image recognition, and medical diagnosis. The goal is to make accurate predictions on new, unseen data based on the learned relationships.