The Split Data module in Azure Machine Learning designer is specifically designed to partition a single dataset into two or more disjoint sets of rows. This is the standard and required procedure for creating training and validation/testing datasets. The module allows for various splitting modes, including a simple percentage split, which is commonly used to allocate a majority of the data for training (e.g., 70-80%) and the remainder for validation or testing.

A. Select Columns in Dataset: This module is used for feature selection by including or excluding specific columns (features), not for partitioning rows into training and validation sets.

B. Add Rows: This module concatenates two datasets by appending rows from one to another. It is used for combining datasets, not splitting one.

D. Join Data: This module merges two datasets based on common key columns, similar to a SQL join. It combines data horizontally, not partitions it into subsets.

1. Microsoft Azure Official Documentation. "Split Data module". Azure Machine Learning Documentation. Microsoft Learn. This document explicitly states

"Use the Split Data module to divide a dataset into two distinct sets. This module is useful when you need to separate data into training and testing sets."

2. Microsoft Azure Official Documentation. "Module reference - Azure Machine Learning". Azure Machine Learning Documentation. Microsoft Learn. This page provides an overview of data manipulation modules

confirming the distinct functions of Split Data (partitioning rows)

Select Columns in Dataset (feature selection)

Add Rows (concatenation)

and Join Data (merging based on keys).