Which of the following best describes the Data Preparation stage's function in data science?

The function of the Data Preparation stage in data science is best described by the option that highlights the process involved in getting data ready for analysis. This stage is critical because it involves multiple tasks such as cleaning, transforming, and structuring the raw data so that it can be effectively used in analytical processes and model building.

During Data Preparation, practitioners address issues like missing values, incorrect formats, and outlier detection which are essential to ensure that subsequent analyses and machine learning models yield reliable and valid results. Without proper preparation, the quality and accuracy of insights derived from the data can be compromised.

The other options reflect different facets of the data science pipeline that occur at different stages. Data collection is just the initial phase and does not encompass the broader scope of preparation. Model building is a later phase that makes use of the prepared data, and data visualization, while important, is generally part of the exploratory data analysis or communication of results rather than the preparation phase itself.