# Cross-Validation: Why One Train/Test Split Lies

> Source: <https://dev.to/dev48v/cross-validation-why-one-traintest-split-lies-4e3m>
> Published: 2026-06-27 12:13:51+00:00

You split your data 80/20, get 91% accuracy, and ship it. But was that 91% luck or skill? A single split can fool you. Cross-validation gives you a trustworthy number. Here's k-fold, visualized.

🔁 **Watch the folds rotate:** [https://dev48v.infy.uk/ml/day18-cross-validation.html](https://dev48v.infy.uk/ml/day18-cross-validation.html)

One train/test split is high-variance: a lucky test set flatters your model, an unlucky one trashes it. You're judging on a single roll of the dice.

Split the data into k equal folds. Then, k times: train on k−1 folds, validate on the held-out one. You get **k scores** — report the **mean ± std**. Every data point gets used for both training and validation (in different rounds), so the estimate is stable.

The demo rotates each fold through validation, fits a real model per fold, fills in the per-fold scores, and shows the average — next to a single split you can reshuffle to watch it swing.

Cost: k× the training. Worth it for an honest score.

🔨 Built from scratch (split into folds → train/score each → mean±std → grid-search) on the page: [https://dev48v.infy.uk/ml/day18-cross-validation.html](https://dev48v.infy.uk/ml/day18-cross-validation.html)

Part of MachineLearningFromZero. 🌐 [https://dev48v.infy.uk](https://dev48v.infy.uk)