Labeling problems in machine learning and data science refer to the challenges associated with providing accurate and meaningful labels to data instances, which are crucial for the performance of supervised learning tasks. Mislabeling, incomplete labels, and the need for domain expertise to assign correct labels can significantly impair model training and prediction accuracy.