Label noise refers to errors or inconsistencies in the labels of a dataset, which can degrade the performance of machine learning models by introducing incorrect information during training. Addressing label noise involves techniques such as noise-tolerant algorithms, data cleaning, and robust loss functions to improve model accuracy and reliability.