Data wrangling involves the process of cleaning, transforming, and organizing raw data into a format suitable for analysis in data science. This stage is essential for ensuring data quality, as it addresses issues such as missing values, inconsistent formats, and outliers. By effectively wrangling data, data scientists can derive meaningful insights, build robust models, and support decision-making. The process often includes steps like data merging, normalization, and preparation for visualization, which set the foundation for successful data analysis.