Add Insights on Model Evaluation and Fine-Tuning Docs Page (#14085)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/guides/index.md

@@ -47,6 +47,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Data Collection and Annotation](data-collection-and-annotation.md) 🚀 NEW: Explore the tools, techniques, and best practices for collecting and annotating data to create high-quality inputs for your computer vision models.
 - [Preprocessing Annotated Data](preprocessing_annotated_data.md) 🚀 NEW: Learn about preprocessing and augmenting image data in computer vision projects using YOLOv8, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA).
 - [Tips for Model Training](model-training-tips.md) 🚀 NEW: Explore tips on optimizing batch sizes, using mixed precision, applying pre-trained weights, and more to make training your computer vision model a breeze.
+- [Insights on Model Evaluation and Fine-Tuning](model-evaluation-insights.md) 🚀 NEW: Gain insights into the strategies and best practices for evaluating and fine-tuning your computer vision models. Learn about the iterative process of refining models to achieve optimal results.
 - [ROS Quickstart](ros-quickstart.md) 🚀 NEW: Learn how to integrate YOLO with the Robot Operating System (ROS) for real-time object detection in robotics applications, including Point Cloud and Depth images.
 
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