Multi-task learning is a subfield of machine learning that focuses on training a single model to perform multiple related learning tasks simultaneously. This approach investigates how leveraging commonalities and differences across tasks can lead to improved learning efficiency and prediction accuracy for all tasks, often by inducing the model to learn generalized representations that benefit multiple objectives. Its significance lies in enhancing model generalization, regularization, and data efficiency compared to training separate models for each task.