[ENH] Optimize ElasticEnsemble _fit to avoid redundant cross-validation

[Nithurshen]

Reference Issues/PRs

Fixes #2854

What does this implement/fix? Explain your changes.

This PR optimizes the _fit method in the ElasticEnsemble classifier to eliminate a redundant cross-validation step, speeding up the fitting process under specific conditions.

Problem

The original implementation first used GridSearchCV or RandomizedSearchCV to find the best parameters for a given distance measure. Then, to get the accuracy score for weighting the ensemble, it performed a second, separate cross_val_predict using those best parameters.

This second cross_val_predict step was redundant when the initial parameter search was already performed on the entire training set (proportion_train_in_param_finding == 1.0), as it was essentially re-running the same validation.

Solution

I've introduced a new logic path that runs only when self.proportion_train_in_param_finding == 1.0 and not self.majority_vote.

The modification:

1. Bypasses GridSearchCV/RandomizedSearchCV entirely.
2. Manually iterates through the parameter grid (respecting self.proportion_of_param_options to mimic the randomized search behavior).
3. Runs cross_val_predict once for each parameter set inside a single loop, calculating the accuracy.
4. Keeps track of the best accuracy score and the corresponding best parameters as it iterates.

This change combines the parameter search and the accuracy-for-weighting calculation into a single loop, completely removing the redundant N-fold cross-validation pass.

The original logic (using GridSearchCV + the second cross_val_predict) is fully preserved for all other cases (i.e., when subsampling for parameter finding or when majority_vote is True), ensuring no existing behavior is broken.

Does your contribution introduce a new dependency? If yes, which one?

No.

Any other comments?

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