Preprocessing techniques are crucial steps in data preparation that enhance the quality of data for analysis and machine learning models, ensuring more accurate and efficient results. These techniques involve cleaning, transforming, and organizing raw data into a structured format suitable for analysis, addressing issues like missing values, noise, and inconsistencies.