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Envelope methodology is succinctly pitched as a class of procedures for increasing efficiency in multivariate analyses without altering traditional objectives \citep[first sentence of page 1]{cook2018introduction}. This description is true with the additional caveat that the efficiency gains obtained by envelope methodology are mitigated by model selection volatility to an unknown degree. The bulk of the current envelope methodology literature does not account for this added variance that arises from the uncertainty in model selection. Recent strides to account for model selection volatility have been made on two fronts: 1) development of a weighted envelope estimator, in the context of multivariate regression, to account for this variability directly; 2) development of a model selection criterion that facilitates consistent estimation of the correct envelope model for more general settings. In this paper, we unify these two directions and provide weighted envelope estimators that directly account for the variability associated with model selection and are appropriate for general multivariate estimation settings for vector valued parameters. Our weighted estimation technique provides practitioners with robust and useful variance reduction in finite samples. Theoretical justification is given for our estimators and validity of a nonparametric bootstrap procedure for estimating their asymptotic variance are established. Simulation studies and a real data analysis support our claims and demonstrate the advantage of our weighted envelope estimator when model selection variability is present.