Supervised learning is a type of machine learning where an algorithm is trained on labeled data, meaning the input data is paired with the correct output. In this learning paradigm, the model makes predictions based on input features and is then corrected by comparing these predictions to the actual outcomes. Through continual adjustments, the algorithm improves its accuracy over time. Supervised learning is commonly used for tasks like classification and regression. Applications include spam detection, image recognition, and predictive analytics, where understanding the relationship between input and output is crucial.