The significance of data annotation in machine learning lies in its role in improving model accuracy and performance. Data annotation involves labeling or tagging data, providing context essential for supervised learning algorithms. Quality annotated data serves as a training foundation, enabling models to recognize patterns and make predictions effectively. As machine learning relies heavily on vast datasets, accurate annotation ensures models are well-trained, leading to better outcomes in applications such as image recognition, natural language processing, and more.