The machine learning approach that involves training a model with labeled data is known as supervised learning. In this method, algorithms learn from a training dataset that includes input-output pairs, allowing the model to identify patterns and relationships within the data. During testing, the trained model can predict outcomes for new, unseen data based on the learned relationships. Supervised learning is commonly used for tasks like classification and regression, helping in applications like image recognition and financial forecasting.