Is cross validation needed? Suppose we have training data set and a test data set. The outcome variable is binary. Is it usually necessary to split the training data set so that there is a cross validation data set? Or can you use the whole training data set to build a model and the use this model on the test data set? For logistic regression, for example, would cross validation really help? If so, what type would be best?
 A: Cross validation has two purposes : 


*

*when you don't use cross validation and randomly select a part of data as train and other part as test, you may have a high accuracy in that part for train and test but when you select another train and test data you may have lower accuracy. Cross validation methods like n-fold cross validation or etc. will help to find best fit model based on your database. with lowest error on all parts of data.

*In some cases cross validation will help to find some parameters of model like C in logistic regression that you can find some documentation about it in MATLAB help center or in R documentation files.
So as we discoursed cross validation has a critical rule to find a reliable model for your database. You should select best cross-validation technique based on your model structure and your sample size. 5-fold cross validation is a well known technique. You can increase the k in k-fold cross validation If you have more sample size. 
A: In general cross validation is always needed when you need to determine the optimal parameters of the model, for logistic regression this would be the $C$ parameter. 
As a first start you can look into k-fold validation, if you are using R look at (http://caret.r-forge.r-project.org/training.html) 
or 
python (http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation)
