When you say "control", I suspect you mean that you have a primary variable of interest, and then you have other variables that are potential confounders. 

In the presence of a confounder, the effect size of the primary variable may appear higher or lower than it actually is (Simpson's Paradoxon / omitted variable bias). To "control" for this effect (see also [here][1]), the confounder must be added to the multiple regression (otherwise you lose the ability to infer the causal effect of the primary variable). 

Note, however, that not all variables should be added to a regression. In some cases, adding a variable can even produce bias (collider). The causal structure determines which variables should go into the regression, regardless of significance or how they affect the estimates of other variables. See more comments [here][2] and in the excellent paper by [Lederer et al., 2019][3]. 


  [1]: https://stats.stackexchange.com/questions/78828/is-there-a-difference-between-controlling-for-and-ignoring-other-variables-i?rq=1
  [2]: https://theoreticalecology.wordpress.com/2019/04/14/mediators-confounders-colliders-a-crash-course-in-causal-inference/
  [3]: https://www.atsjournals.org/doi/full/10.1513/AnnalsATS.201808-564PS?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed