Data labeling is the process of annotating or tagging data to make it understandable for machine learning models. This involves assigning labels to various types of data, such as images, text, or audio, so that algorithms can learn from them. For example, in image recognition, labeling might involve identifying objects within pictures, like cats or cars. The quality of data labeling is crucial for the success of machine learning projects. Accurate labels help improve the performance of models, enabling them to make better predictions or classifications. This process can be done manually by humans or automatically using software tools, but human oversight is often necessary to ensure precision.