Labeling in machine learning refers to the process of annotating data with specific tags or categories that represent the desired output. This is essential for supervised learning, where models learn from labeled examples to make predictions. Proper labeling enhances model accuracy and performance, making it crucial for tasks like image recognition and text classification.