def kfoldcv():
    """
    Returns a detailed, advanced, exam-ready explanation of K-Fold Cross Validation
    including the motivation, workflow, and a fold-wise accuracy table.
    """

    summary = """
K-Fold Cross Validation — Detailed Explanation

K-Fold Cross Validation is a robust model validation technique designed to overcome
the limitations of a single train–test split. Instead of relying on one arbitrary split,
the dataset is partitioned into K equal folds. The model is trained and tested K times,
each time using a different fold as the test set and the remaining (K−1) folds for training.
This rotation ensures every sample is used for training and testing exactly once.

Why It Is Important:
- It reduces variance in performance estimates.
- It prevents misleading accuracy caused by a “lucky” or “unlucky” train-test split.
- It is especially valuable when datasets are small or moderately sized.
- It helps detect overfitting by showing how consistent the model is across folds.

How It Works:
1. Shuffle the dataset and split into K folds (equal size).
2. For each iteration i:
     - Train on all folds except fold i.
     - Test on fold i.
3. Record the accuracy (or any metric) for each fold.
4. Average these K values → Final CV score.

Why It Works:
K-Fold CV simulates K different “real-world” scenarios where the model encounters 
slightly different distributions of training and testing data. Consistency across these 
scenarios indicates strong generalization.

Example (K = 5):

Accuracy Across 5 Folds:
+---------+----------------+
|  Fold   |    Accuracy    |
+---------+----------------+
|   F1    |     84.63%     |
|   F2    |     86.12%     |
|   F3    |     85.47%     |
|   F4    |     89.08%     |
|   F5    |     84.95%     |
+---------+----------------+
Final Average Accuracy = 86.05%

Interpretation:
- The scores are consistent across folds.
- No extreme highs or lows → model generalizes well.
- The final CV score (86.05%) is a more trustworthy performance estimate than
  using a single train-test split.

Insight:
K-Fold acts like taking the model through multiple “parallel-world” evaluations,
giving a stable estimate of real performance.
    """

    return summary
