A little bit on terms first. By definition control variable is kept constant through the study, so you can't use it in regression. You probably mean variables that should be statistically controlled for. Such as covariates or blocking factors (as after randomized block experimental design)
People run regression or ANOVA with such variables not only to wash their effect off predictor variables but mainly to check whether their own effect is significant. If it is significant then their inclusion in the model is fully warranted. If not, they might better be excluded from the model.
This is mostly important for a blocking factor. If you leave it in the model despite that it is not significant you risk to miss the effect of predictor variables due to decreas in Error term df, - blocking factor decreases both Error and its df, and there appeares a competitive situation. Significance of predictors may go down or up depending on "what wins" - fall of Error sum-of-squares of fall of its df. This may be the reason why people prefer more concise models sometimes.
Another reason for this may be that for sample as moderate as 100 inclusuion a lot of IVs, even if they all seem important or significant, lead to overfitting.