A. Lemmatization in data preprocessing reduces words to their base or root form (lemma) by considering the context and part of speech. Unlike stemming, which often cuts off word endings, lemmatization uses a dictionary to transform words into meaningful forms. For example, "running" becomes "run," and "better" becomes "good." It helps maintain meaningful word variants, improving the quality of text analysis and model performance.