The article discusses how to choose the best model in complex data sets with many parameters. Traditional methods can be unreliable, even without extra parameters. The authors developed new criteria that penalize complex models more heavily, leading to better choices. They tested these criteria on a specific example and found that they help to avoid selecting too many or too few parameters, making the selection process more consistent.