Data Wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis. This involves transforming raw data into a usable format by addressing issues such as missing values, inconsistencies, and errors. Data wrangling includes tasks like data cleaning, normalization, merging, and reshaping. It is a critical step in the data preparation process, ensuring that data is accurate, consistent, and ready for analysis, thereby enabling better decision-making and insights.