Data labeling is the process of adding labels or annotations to data, usually in the form of text, images, or audio, to make it easier to analyze, categorize, and use for machine learning purposes. The labels can include anything from simple binary classifications, such as "yes" or "no", to more complex annotations, such as entity recognition, sentiment analysis, or object detection.

Data labeling is a crucial step in the machine learning process, as it is essential for training and testing supervised learning models. Without accurate and consistent labeling, machine learning algorithms would not be able to recognize patterns or make predictions based on the data.