The type of machine learning that uses labeled data for training algorithms is known as supervised learning. In this approach, models learn from a training dataset where the input data is paired with corresponding output labels. The algorithm identifies patterns and relationships within the data, enabling it to make predictions or classifications when given new, unseen data. Supervised learning is widely used in applications such as image recognition, spam detection, and sentiment analysis, where accurate output is crucial for decision-making and operational effectiveness.