  In the framework of an abstract statistical model we discuss how to use the
solution of one estimation problem ({\it Problem A}) in order to construct an
estimator in another, completely different, {\it Problem B}. As a solution of
{\it Problem A} we understand a data-driven selection from a given family of
estimators $\mathbf{A}(\mH)=\big\{\widehat{A}_\mh, \mh\in\mH\big\}$ and
establishing for the selected estimator so-called oracle inequality.
%parameterized by some se t$\mH$. If $\hat{\mh}\in\mH$ is the selected
parameter and $\mathbf{B}(\mH)=\big\{\widehat{B}_\mh, \mh\in\mH\big\}$ is an
estimator's collection built in {\it Problem B} we suggest to use the estimator
$\widehat{B}_{\hat{\mh}}$. We present very general selection rule led to
selector $\hat{\mh}$ and find conditions under which the estimator
$\widehat{B}_{\hat{\mh}}$ is reasonable. Our approach is illustrated by several
examples related to adaptive estimation.
