Tokenization is a crucial step in natural language processing (NLP) that involves dividing text into smaller, manageable units called tokens. These tokens can be words, phrases, or even characters, depending on the granularity required for a particular application. Tokenization serves as a preprocessing step for various NLP tasks, including sentiment analysis, machine translation, and information retrieval. By transforming raw text into structured tokens, algorithms can analyze and interpret language more effectively. Different tokenization methods, such as whitespace tokenization, punctuation-based tokenization, and subword tokenization, cater to specific linguistic needs, improving the performance of machine learning models in understanding and generating human language.