title: What is the motivation for cross validation on feature selection preprocessing?
body: <p>I saw several articles and examples of feature selection (wrapper &amp; embedded methods) where they split the samples data into train and test sets.</p>
  
  <p>I understand why we need to use cross-validate (split the data into train &amp;test set) for building and testing the score of the models (actual prediction of the propose algorithm).</p>
  
  <p>But I can't understand what is the motivation to do so for feature selection ?</p>
  
  <p>There are no true results of which features we need to choose, so how can it improve the process of feature selection  ?</p>
  
  <p>What is the benefit ?</p>