Data annotation and labeling play a crucial role in machine learning because they serve as the foundation for training and evaluating machine learning models. This includes assigning meaningful information or tags to raw data, such as images, text, audio, or video, to create a structured dataset that can be used by algorithms to learn patterns and make predictions. Data annotation and labeling enable supervised learning, improve model performance and interpretability, facilitate transfer learning and active learning, and provide a basis for evaluation and benchmarking. High-quality, accurately labeled data is essential for developing robust and reliable machine learning models that can be deployed in real-world applications.