Supervised learning is a type of machine learning in which an algorithm learns to predict an output variable based on input data that is already labeled with the correct output. In supervised learning, the algorithm is trained on a labeled dataset, which consists of input-output pairs. The algorithm then uses this labeled data to learn patterns and relationships between the input and output variables. Once the algorithm is trained, it can be used to predict the output for new input data that has not been seen before. Supervised learning is commonly used in applications such as image recognition, speech recognition, and natural language processing.