The correct option is Transforming and cleaning raw datasets into structured formats suitable for machine learning models.

This is what data wrangling means in a machine learning environment. It covers the inspection and cleaning of raw inputs and the conversion of types and formats so the data can be consumed by models. Typical tasks include parsing messy text, handling missing or inconsistent values, encoding categorical fields, reshaping and joining datasets, and creating features for modeling.

Splitting datasets into training testing and validation subsets is not data wrangling. That action is a separate step focused on evaluation and is usually performed after the data has been cleaned and prepared.

Managing storage governance and sharing of data among analysts and teams describes data governance and collaboration rather than the technical cleaning and transformation work that data wrangling entails.

Being identical to simple preprocessing tasks such as scaling and basic imputations is incorrect because preprocessing can be one part of wrangling but the broader process also includes discovery, complex transformations, feature engineering, and combining multiple sources.

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