Multitask Learning (MTL) is a machine learning approach that enables a model to learn multiple tasks simultaneously rather than training separate models for each task. This technique leverages shared representations and knowledge transfer between related tasks, which can enhance overall performance and reduce the risk of overfitting. By learning tasks in parallel, MTL allows for more efficient use of data and computational resources. It is particularly beneficial in scenarios where tasks are correlated, as the insights gained from one task can inform and improve the learning of others, leading to better generalization and robustness in model performance.