Choose a validation split that matches the question

A split is an assumption about new data

The scikit learn guide describes cross validation and split strategies for different data arrangements. A random division of rows is only one option. The relationship between samples matters when choosing a validation scheme. Repeated measurements from the same source, grouped records, and chronological observations require particular attention before treating rows as independent examples. [1]

Specify the unit of independence

Write down what counts as a new case in the intended workflow. For experimental measurements, several rows may belong to one specimen or acquisition session. For a business forecast, a future period is different from a random row taken from the same historical interval. Use this description to guide the split rather than choosing a method only because it produces a favorable score.

Our suggested experiment record includes the dataset version, grouping fields, split configuration, preprocessing, model settings, and evaluation outputs. Reserve a final assessment set when the project needs one. Keep tuning decisions separate from that final assessment. Review class or group coverage so the split does not quietly remove difficult parts of the task.

Interpret variation between folds

Compare more than the mean score. Inspect the spread of results and the errors in individual groups. A large difference between groups can reveal a collection issue or a mismatch with the deployment setting. Report the validation scheme beside the metric, and explain which kinds of new cases it represents. A model result is easier to assess when the reader can reconstruct the evaluation rather than relying on a single number.