Supervised learning is a type of machine learning where a model is trained on labeled data. This means that the input data is paired with the correct output, allowing the model to learn the relationship between them. For example, in a supervised learning task, a model might learn to identify images of cats and dogs by being shown many labeled examples of each. The goal of supervised learning is to make accurate predictions on new, unseen data. Once trained, the model can apply what it has learned to classify or predict outcomes based on new inputs. This approach is widely used in applications like spam detection and image recognition.