Contributed documentation-driven enhancements to the sktime/sktime repository, focusing on improving forecasting usage guidance for end users. Leveraging expertise in Python, data science, and machine learning, delivered two targeted updates that added comprehensive usage examples to the ForecastingRandomizedSearchCV docstring and improved the readability of ForecastingGridSearchCV documentation. These changes aligned the structure and content across related forecasting classes, reducing user confusion and streamlining the onboarding process for model selection workflows. The work was limited to documentation, ensuring no impact on code logic or regression risk, and prioritized clarity, consistency, and ease of review for both new and experienced users.