Greetings in this segment of Introduction to Modeling Techniques for Text Classification! This part focuses on the heart of preprocessing techniques in modeling — Train-Test Split and Stratified Cross-Validation.

Rails of any machine learning model are laid by creating an effective split in the dataset and ensuring class balance. You'll not just learn about these core concepts but also implement them using Python's powerful library, scikit-learn. Using these techniques, you'll split the SMS Spam Collection dataset for effective text classification later in the course.

In real life, as you browse your inbox, you come across various legitimate (ham) and promotional or unsolicited (spam) messages. Machine Learning models help distinguish between these, by labeling an incoming message as spam or ham. A good model is crucial for avoiding a cluttered inbox.

Let's start by loading the dataset. The datasets library can pull the data directly, and we'll convert it into a pandas DataFrame for easier data manipulation.