Disentangled representations refer to a way of structuring data in machine learning models where distinct features or factors of variation in the data are captured by separate dimensions, allowing for a more interpretable and flexible understanding of the data. This approach improves model generalization, transfer learning, and robustness by enabling the model to focus on independent aspects of data variation, rather than entangled combinations of such features.